





'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import numpy as np
					import tensorflow as tf

					from transformers import (
					    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    FlaubertConfig,
					    TFFlaubertForMultipleChoice,
					    TFFlaubertForQuestionAnsweringSimple,
					    TFFlaubertForSequenceClassification,
					    TFFlaubertForTokenClassification,
					    TFFlaubertModel,
					    TFFlaubertWithLMHeadModel,
					)
class 							__SCREAMING_SNAKE_CASE    :






				"""simple docstring"""







				def __init__(    self	:   Dict   ,							__a	:   int   ,							):
								_a				      =					parent
								_a				      =					13
								_a				      =					7
								_a				      =					True
								_a				      =					True
								_a				      =					True
								_a				      =					True
								_a				      =					True
								_a				      =					False
								_a				      =					False
								_a				      =					False
								_a				      =					2
								_a				      =					99
								_a				      =					0
								_a				      =					32
								_a				      =					2
								_a				      =					4
								_a				      =					0.1
								_a				      =					0.1
								_a				      =					5_12
								_a				      =					16
								_a				      =					2
								_a				      =					0.02
								_a				      =					3
								_a				      =					4
								_a				      =					"last"
								_a				      =					True
								_a				      =					None
								_a				      =					0




				def     UpperCamelCase__  (    self	:   Any   ):
								_a				      =					ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size   )
								_a				      =					random_attention_mask([self.batch_size, self.seq_length]   ,							dtype=tf.floataa   )

								_a				      =					None
								if self.use_input_lengths:
												_a				      =					(
												    ids_tensor([self.batch_size]   ,							vocab_size=2   ) + self.seq_length - 2
												)  # small variation of seq_length

								_a				      =					None
								if self.use_token_type_ids:
												_a				      =					ids_tensor([self.batch_size, self.seq_length]   ,							self.n_langs   )

								_a				      =					None
								_a				      =					None
								_a				      =					None
								if self.use_labels:
												_a				      =					ids_tensor([self.batch_size]   ,							self.type_sequence_label_size   )
												_a				      =					ids_tensor([self.batch_size, self.seq_length]   ,							self.num_labels   )
												_a				      =					ids_tensor([self.batch_size]   ,							2   ,							dtype=tf.floataa   )
												_a				      =					ids_tensor([self.batch_size]   ,							self.num_choices   )

								_a				      =					FlaubertConfig(
								    vocab_size=self.vocab_size   ,							n_special=self.n_special   ,							emb_dim=self.hidden_size   ,							n_layers=self.num_hidden_layers   ,							n_heads=self.num_attention_heads   ,							dropout=self.hidden_dropout_prob   ,							attention_dropout=self.attention_probs_dropout_prob   ,							gelu_activation=self.gelu_activation   ,							sinusoidal_embeddings=self.sinusoidal_embeddings   ,							asm=self.asm   ,							causal=self.causal   ,							n_langs=self.n_langs   ,							max_position_embeddings=self.max_position_embeddings   ,							initializer_range=self.initializer_range   ,							summary_type=self.summary_type   ,							use_proj=self.use_proj   ,							bos_token_id=self.bos_token_id   ,							)

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    input_lengths,
								    sequence_labels,
								    token_labels,
								    is_impossible_labels,
								    choice_labels,
								    input_mask,
								)




				def     UpperCamelCase__  (    self	:   Tuple   ,							__a	:   Tuple   ,							__a	:   List[str]   ,							__a	:   Optional[Any]   ,							__a	:   Any   ,							__a	:   Union[str, Any]   ,							__a	:   Union[str, Any]   ,							__a	:   Dict   ,							__a	:   Tuple   ,							__a	:   Optional[int]   ,							):
								_a				      =					TFFlaubertModel(config=__a   )
								_a				      =					{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
								_a				      =					model(__a   )

								_a				      =					[input_ids, input_mask]
								_a				      =					model(__a   )
								self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)   )




				def     UpperCamelCase__  (    self	:   str   ,							__a	:   Union[str, Any]   ,							__a	:   str   ,							__a	:   Tuple   ,							__a	:   Dict   ,							__a	:   Tuple   ,							__a	:   Optional[Any]   ,							__a	:   Dict   ,							__a	:   List[str]   ,							__a	:   Any   ,							):
								_a				      =					TFFlaubertWithLMHeadModel(__a   )

								_a				      =					{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
								_a				      =					model(__a   )

								self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.seq_length, self.vocab_size)   )




				def     UpperCamelCase__  (    self	:   List[str]   ,							__a	:   Optional[Any]   ,							__a	:   Any   ,							__a	:   str   ,							__a	:   Tuple   ,							__a	:   Optional[int]   ,							__a	:   int   ,							__a	:   Union[str, Any]   ,							__a	:   Optional[int]   ,							__a	:   Optional[Any]   ,							):
								_a				      =					TFFlaubertForQuestionAnsweringSimple(__a   )

								_a				      =					{"input_ids": input_ids, "lengths": input_lengths}

								_a				      =					model(__a   )

								self.parent.assertEqual(result.start_logits.shape   ,							(self.batch_size, self.seq_length)   )
								self.parent.assertEqual(result.end_logits.shape   ,							(self.batch_size, self.seq_length)   )




				def     UpperCamelCase__  (    self	:   Union[str, Any]   ,							__a	:   Dict   ,							__a	:   Any   ,							__a	:   Tuple   ,							__a	:   List[str]   ,							__a	:   str   ,							__a	:   Optional[int]   ,							__a	:   Dict   ,							__a	:   Dict   ,							__a	:   Optional[Any]   ,							):
								_a				      =					TFFlaubertForSequenceClassification(__a   )

								_a				      =					{"input_ids": input_ids, "lengths": input_lengths}

								_a				      =					model(__a   )

								self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.type_sequence_label_size)   )




				def     UpperCamelCase__  (    self	:   Tuple   ,							__a	:   Union[str, Any]   ,							__a	:   Any   ,							__a	:   List[str]   ,							__a	:   Dict   ,							__a	:   int   ,							__a	:   Tuple   ,							__a	:   Optional[int]   ,							__a	:   int   ,							__a	:   List[Any]   ,							):
								_a				      =					self.num_labels
								_a				      =					TFFlaubertForTokenClassification(config=__a   )
								_a				      =					{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
								_a				      =					model(__a   )
								self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.seq_length, self.num_labels)   )




				def     UpperCamelCase__  (    self	:   int   ,							__a	:   Union[str, Any]   ,							__a	:   int   ,							__a	:   Tuple   ,							__a	:   int   ,							__a	:   str   ,							__a	:   Dict   ,							__a	:   Any   ,							__a	:   Optional[int]   ,							__a	:   List[Any]   ,							):
								_a				      =					self.num_choices
								_a				      =					TFFlaubertForMultipleChoice(config=__a   )
								_a				      =					tf.tile(tf.expand_dims(__a   ,							1   )   ,							(1, self.num_choices, 1)   )
								_a				      =					tf.tile(tf.expand_dims(__a   ,							1   )   ,							(1, self.num_choices, 1)   )
								_a				      =					tf.tile(tf.expand_dims(__a   ,							1   )   ,							(1, self.num_choices, 1)   )
								_a				      =					{
								    "input_ids": multiple_choice_inputs_ids,
								    "attention_mask": multiple_choice_input_mask,
								    "token_type_ids": multiple_choice_token_type_ids,
								}
								_a				      =					model(__a   )
								self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_choices)   )




				def     UpperCamelCase__  (    self	:   str   ):
								_a				      =					self.prepare_config_and_inputs()
								(
								    (
								    _a   
								)      ,   (
								    _a   
								)      ,   (
								    _a   
								)      ,   (
								    _a   
								)      ,   (
								    _a   
								)      ,   (
								    _a   
								)      ,   (
								    _a   
								)      ,   (
								    _a   
								)      ,   (
								    _a   
								)      ,      
								)				      =					config_and_inputs
								_a				      =					{
								    "input_ids": input_ids,
								    "token_type_ids": token_type_ids,
								    "langs": token_type_ids,
								    "lengths": input_lengths,
								}
								return config, inputs_dict


@require_tf
class 							__SCREAMING_SNAKE_CASE    (lowerCamelCase_			,				lowerCamelCase_			,				unittest.TestCase ):






				"""simple docstring"""







				__a  =(
				    (
				        TFFlaubertModel,
				        TFFlaubertWithLMHeadModel,
				        TFFlaubertForSequenceClassification,
				        TFFlaubertForQuestionAnsweringSimple,
				        TFFlaubertForTokenClassification,
				        TFFlaubertForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				__a  =(
				    (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
				)  # TODO (PVP): Check other models whether language generation is also applicable
				__a  =(
				    {
				        'feature-extraction': TFFlaubertModel,
				        'fill-mask': TFFlaubertWithLMHeadModel,
				        'question-answering': TFFlaubertForQuestionAnsweringSimple,
				        'text-classification': TFFlaubertForSequenceClassification,
				        'token-classification': TFFlaubertForTokenClassification,
				        'zero-shot': TFFlaubertForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				__a  =False
				__a  =False




				def     UpperCamelCase__  (    self	:   Tuple   ,							__a	:   Optional[int]   ,							__a	:   Optional[Any]   ,							__a	:   List[Any]   ,							__a	:   Optional[int]   ,							__a	:   int   ):


								if (
								    pipeline_test_casse_name == "QAPipelineTests"
								    and tokenizer_name is not None
								    and not tokenizer_name.endswith("Fast"   )
								):
												# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
												# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
												# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
												return True

								return False




				def     UpperCamelCase__  (    self	:   Tuple   ):
								_a				      =					TFFlaubertModelTester(self   )
								_a				      =					ConfigTester(self   ,							config_class=__a   ,							emb_dim=37   )




				def     UpperCamelCase__  (    self	:   Union[str, Any]   ):
								self.config_tester.run_common_tests()




				def     UpperCamelCase__  (    self	:   str   ):
								_a				      =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_model(*__a   )




				def     UpperCamelCase__  (    self	:   List[Any]   ):
								_a				      =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_lm_head(*__a   )




				def     UpperCamelCase__  (    self	:   Tuple   ):
								_a				      =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_qa(*__a   )




				def     UpperCamelCase__  (    self	:   Optional[Any]   ):
								_a				      =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_sequence_classif(*__a   )




				def     UpperCamelCase__  (    self	:   str   ):
								_a				      =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_for_token_classification(*__a   )




				def     UpperCamelCase__  (    self	:   Union[str, Any]   ):
								_a				      =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_for_multiple_choice(*__a   )




				@slow
				def     UpperCamelCase__  (    self	:   Optional[int]   ):


								for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_a				      =					TFFlaubertModel.from_pretrained(__a   )
												self.assertIsNotNone(__a   )


@require_tf
@require_sentencepiece
@require_tokenizers
class 							__SCREAMING_SNAKE_CASE    (unittest.TestCase ):






				"""simple docstring"""







				@slow
				def     UpperCamelCase__  (    self	:   List[str]   ):
								_a				      =					TFFlaubertModel.from_pretrained("jplu/tf-flaubert-small-cased"   )

								_a				      =					tf.convert_to_tensor(
								    [[0, 1_58, 7_35, 25_92, 14_24, 67_27, 82, 1]]   ,							dtype=tf.intaa   ,							)  # "J'aime flaubert !"

								_a				      =					model(__a   )[0]
								_a				      =					tf.TensorShape((1, 8, 5_12)   )
								self.assertEqual(output.shape   ,							__a   )
								# compare the actual values for a slice.
								_a				      =					tf.convert_to_tensor(
								    [
								        [
								            [-1.8768773, -1.566555, 0.27072418],
								            [-1.6920038, -0.5873505, 1.9329599],
								            [-2.9563985, -1.6993835, 1.7972052],
								        ]
								    ]   ,							dtype=tf.floataa   ,							)

								self.assertTrue(np.allclose(output[:, :3, :3].numpy()   ,							expected_slice.numpy()   ,							atol=1e-4   )   )




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						super().__init__()
						self.register_modules(unet=snake_case   ,    scheduler=snake_case  )







			@torch.no_grad()
			def __call__(				self   ,    snake_case = 1   ,    snake_case = None   ,    snake_case = 50   ,    snake_case = "pil"   ,    snake_case = True   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,    generator=snake_case   ,    )
						UpperCamelCase__       =							image.to(self.device  )

						# set step values
						self.scheduler.set_timesteps(snake_case  )

						for t in self.progress_bar(self.scheduler.timesteps  ):
									# 1. predict noise model_output
									UpperCamelCase__       =							self.unet(snake_case   ,    snake_case  ).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									UpperCamelCase__       =							self.scheduler.step(snake_case   ,    snake_case   ,    snake_case  ).prev_sample

						UpperCamelCase__       =							(image / 2 + 0.5).clamp(0   ,    1  )
						UpperCamelCase__       =							image.cpu().permute(0   ,    2   ,    3   ,    1  ).numpy()
						if output_type == "pil":
									UpperCamelCase__       =							self.numpy_to_pil(snake_case  )

						if not return_dict:
									return (image,), "This is a local test"

						return ImagePipelineOutput(images=snake_case  ), "This is a local test"


